## [1] 0.03980539
## [1] 2.361799

## # A tibble: 10 x 2
##        t r_effective
##    <dbl>       <dbl>
##  1     1        2.5 
##  2     2        2.46
##  3     3        2.36
##  4     4        2.36
##  5     5        2.36
##  6     6        2.36
##  7     7        2.36
##  8     8        2.36
##  9     9        2.36
## 10    10        2.36

## # A tibble: 42 x 20
##    r_effective prop_identified alpha     R kappa   eta    nu  t_ds  t_da t_qcs
##          <dbl>           <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
##  1        2.41          0.0654   0.2   2.5   0.5   0.5     4     1     3     3
##  2        2.11          0.271    0.2   2.5   0.5   0.5     4     1     3     3
##  3        1.72          0.509    0.2   2.5   0.5   0.5     4     1     3     3
##  4        2.42          0.0680   0.2   2.5   0.5   0.5     4     2     3     3
##  5        2.14          0.281    0.2   2.5   0.5   0.5     4     2     3     3
##  6        1.79          0.522    0.2   2.5   0.5   0.5     4     2     3     3
##  7        2.43          0.0699   0.2   2.5   0.5   0.5     4     3     3     3
##  8        2.17          0.288    0.2   2.5   0.5   0.5     4     3     3     3
##  9        1.84          0.529    0.2   2.5   0.5   0.5     4     3     3     3
## 10        2.43          0.0711   0.2   2.5   0.5   0.5     4     4     3     3
## # … with 32 more rows, and 10 more variables: t_qca <dbl>, t_qhs <dbl>,
## #   t_qha <dbl>, t_q <dbl>, omega_c <dbl>, omega_h <dbl>, omega_q <dbl>,
## #   quarantine_days <dbl>, rho_s <dbl>, rho_a <dbl>

grid <- grid %>%
  mutate(
    rho_a = pmin(rho_s * 0.5, 1),
    t_da = t_ds,
    t_qcs = t_ds,
    t_qca = t_ds,
    t_qhs = t_ds,
    t_qha = t_ds,
    t_q = t_ds
  )